Image databases represent a core component of many well-established and emerging applications and services including ecommerce and security. For example, image databases of faces, fingerprints, and eye retinas are used extensively for biometric and other security-related applications. Such databases store a vast number of images of the same type, and yet, traditional compression standards are used to compress and store these images without exploiting the correlation that potentially exists among the images within the same database. For example, the ISO/IEC 19794 standard on biometric data interchange format defined JPEG and JPEG2000 as admissible lossy compression methods. A key driver for encoding each image in isolation of other images within the same database is the ability to access and decode any image without the need to access/decode other images. Such requirement eliminates popular video coding standards as viable candidates for coding still-image databases.
In this disclosure, it is proposed to employ a tensor-decomposition framework that can achieve both: (a) random access to any image within a collection of images coded jointly and (b) coding efficiency by exploiting any potential correlation that may exist among the images within the same database. To bring focus to the problem addressed here, an image ensemble is defined as a set of images of the same type (e.g., images of human faces). Thus, the goal is to develop a compression approach for image ensembles while achieving full random access. The proposed tensor-based framework can access any image within an ensemble at different levels of quality (and corresponding scalable bitrates) without the need to reconstruct or access any other image from the same ensemble. This is crucial, not only for storage efficiency, but also to reduce bandwidth across networks for scalable search and retrieval engines. Experimental results show the viability of the proposed tensor-based framework for image-ensemble compression.
This section provides background information related to the present disclosure which is not necessarily prior art.